ERROR CORRECTION MODEL - WordPress.com€¦ · ERROR CORRECTION MODEL Yule (1936) and Granger and Newbold (1974) were the first to draw attention to the problem of false correlations

ECM Model Regression Teaching Materials Agus Tri Basuki, M.Sc.

ERROR CORRECTION MODEL Yule (1936) and Granger and Newbold (1974) were the first to draw

attention to the problem of false correlations and find solutions about how to

overcome them in time series analysis. Providing two time series that are

completely unrelated but integrated (not stationary), regression analysis with each

other will tend to produce relationships that appear to be statistically significant

and a researcher might think he has found evidence of a correct relationship

between these variables. Ordinary least squares are no longer consistent and the

test statistics commonly used are invalid. Specifically, the Monte Carlo simulation

shows that one will get very high t-squared R statistics, very high and low Durbin-

Watson statistics. Technically, Phillips (1986) proves that parameter estimates

will not converge in probability, intercepts will diverge and slope will have a

distribution that does not decline when the sample size increases. However, there

may be a general stochastic tendency for both series that a researcher is really

interested in because it reflects the long-term relationship between these variables.

Because of the stochastic nature of trends, it is not possible to break

integrated series into deterministic trends (predictable) and stationary series that

contain deviations from trends. Even in a random walk detrended detrended

random correlation will finally appear. So detrending doesn't solve the estimation

problem.

To keep using the Box-Jenkins approach, one can differentiate series and

then estimate models such as ARIMA, given that many of the time series that are

commonly used (eg in economics) seem stationary in the first difference.

Forecasts from such models will still reflect the cycles and seasonality in the data.

However, any information about long-term adjustments that may contain data at

the level level and long-term estimates will not be reliable. This prompted Sargan

(1964) to develop an ECM methodology, which maintains level information

Before carrying out ECM estimation and descriptive analysis, several

stages must be carried out such as data stationarity test, and degree of

cointegration test. After the data is estimated using ECM. The steps in formulating

the ECM model are as follows:

1. Specify the expected relationship in the model under study.

GDPt = f(INF, LIR, KURS, AK, GFCF, IVA, TRADE, POP, TR)

GDPt = 0 + 1INFt + 2LIRt + 3KURSt + 5GFCFt + 6IVAt + 7TRADEt + 8AKt

+ 9TRt ....................................................... (1) Information:

GDPt : Gross Domestic Product per year in period t

INFt : Inflation, consumer prices (annual%) in period t

LIRt : Lending interest rate (%) period t

Kurst : Rupiah Exchange Rate against US dollar period t

GFCFt : Gross Fixed Capital Formation in period t

IVAt : Industry, value added (constant LCU) in period t

TRADEt : Total Trade Value in period t

Akt : Labor Force in period t


TRt : Tax Revenue (current LCU) in period t

0, 1, 2,.... 9 : Coeffisient in the long-term

2. Establish a single cost function in the error correction method:

Ct = b1 (GDPt – GDPt*) + b2 {(GDPt - GDPt-1)– ft (Zt - Zt-1)}2

…........... (2)

Based on the data above Ct is a quadratic cost function, GDPt is gross

domestic income in period t, whereas Zt is a vector variable that affects gross

domestic income and is considered to be affected linearly by inflation,

interest rates, exchange rates, labor force, total investment in the country's

economy host, Industry, value added, total trade value, total population, and

tax revenue. b1 and b2 are row vectors that give weight to Zt - Zt-1.

The first component of the single cost function above is the imbalance cost

and the second component is the adjustment cost component. Whereas B is a

time lag operation. Zt is a variable factor that influences currency demand.

a. Minimizing the cost function of the equation with respect to Rt, you will

get:

GDPt = GDPt + (1- e) GDPt-1 – (1 – e) ft (1-B) Zt ......................................... ( 3)

b. Substitute GDPt - GDPt-1 so that it is obtained:

LogGDPt = b0 + b1INFt + b2LIRt + b3LogKURSt + b5LogGFCFt + b6LogIVAt + B7LogTRADEt + b8LogAKt + b9Log0TRt ..................................................... (4)

While the short-term relationship is expressed by the following

equation: DLogGDPt = b0 + b1DINFt + b2DLIRt + b3DLogKURSt + b5DLogGFCFt + b6DLogIVAt + B7DLogTRADEt + b8DLogAKt + b9DLog0TRt ..................... (5) From the results of parameterization of the short-term equation can

produce a new equation form, the equation was developed from the

previous equation to measure the long-term parameters by using ECM

model econometric regression:

DLogGDPt = β0 + b1DINFt + β2DLIRt + β3DLogKURSt + β5DLogGFCFt + β6

DLogIVAt + β7DLogTRADEt + β8DLogAKt + β9DLog0TRt + ECT + t ….. (6) ECT = DINFt-1 + DLIRt-1 + DLogKURSt-1 + DLogGFCFt-1 + DLogIVAt-1 + DLogTRADEt-1 + DLogAKt-1 + DLog0TRt-1 …................................................. (7) Note : ECT : Error Correction Term


Decreased Stages of the ECM Model

Unit Root Test (unit root test)

a. The concept used to test the stationary of a time-consuming data is the unit

root test. If a time series data is not stationary, it can be said that the data is

facing a unit root problem.

b. The existence of the unit root problem can be seen by comparing the t-

statistics results of the regression with the value of the Augmented Dickey

Fuller test. The equation model is as follows:

c. ΔGDPt = a1 + a2 T + ΔGDPt-1 + i ∑mi=1GDPt-1 + et ………............... (9)

d. Where ΔGDPt-1 = (ΔGDPt-1 - ΔGDPt-2) and so on, m = length of time lag based

on i = 1,2 .... m. The null hypothesis is still δ = 0 or ρ = 1. The t-statistics

value of ADF is the same as the t-statistic value of DF.

Integration Test

a. If the unit root test above the time series data observed is not stationary,

then the next step is to test the degree of integration to find out at what

degree of integration the data will be stationary. The degree of integration

test is carried out with the model:

b. ΔGDPt = a1 + δΔGDPt-1 + i ∑mi=1GDPt-1 + et ....................... (10)

c. ΔGDPt = β 1 + β 2 T + δΔGDPt-1 + i ∑mi=1GDPt-1 + et …………......... (11)

d. The t-statistic value of the regression results of equations (10) and (11) is

compared with the t-statistic value in the DF table. If the value of δ in both

equations is equal to one, the variable ΔUKRt is said to be stationary at

one degree, or symbolized as ΔGDPt ~ I (1).

Cointegration Test

The cointegration test is most commonly used the engle-Granger (EG) test, the

augmented Engle-Granger test (AEG) and the Durbin-Watson cointegrating

regression test (CRDW). To get the calculated EG, AEG and CRDW values,

the data to be used must have been integrated to the same degree. OLS testing

of an equation below:

LogGDPt = b0 + b1INFt + b2LIRt + b3 LogKURSt + b5LogGFCFt +

b6LogIVAt + B7 LogTRADEt + b8 LogAKt + b9 Log0TRt ............... (12)

From equation (12), save the residual (error terms). The next step is to

estimate the autoregressive equation model of the residual based on the

following equations:

Δt = λt-1 ………………..........................................(13)

Δt = λt-1 + i t-1 .................................................................(14)


With the hypothesis test:

H0 : = I(1), meaning that there is no cointegration

Ha : I(1), meaning that there is cointegration

Based on the OLS regression results in equation (12) will obtain a calculated

CRDW value (DW value in the equation) to then be compared with the

CRDW table. While from equations (13) and (14) EG and AEG values will be

obtained which will also be compared with the DF and ADF table values.

Error Correction Model

If it passes the cointegration test, it will then be tested by using a dynamic

linear model to find out the possibility of structural change, because the long-

term equilibrium relationship between the independent variable and the

dependent variable from the results of the cointegration test will not apply at

any time. In brief, the ECM operation process in the currency demand

equation (5) has been modified to:

DLogGDPt = β0 + b1DINFt + β2DLIRt + β3DLogKURSt + β5DLogGFCFt + β6 DLogIVAt

+ β7 DLogTRADEt + β8DLogAKt + β9DLog0TRt + ECT(-1) + t ............. (13)


Yes

No

Literature Study

(Previous Theories and Studies)

Identification of Research Variables and

Modeling

Making Hypotheses

Data processing

Unit Root Test, Cointegration Test, Short-

Term Regression and Classical Assumption

Test

Data Collection Process

Model Estimation and

Hypothesis test

Conclusions and Recommendations

Figure 1

Steps for Research with ECM

Revition

Fullfil


Data of GDP, inf, lir, kurs, GFCF, IVA, TRADE, AK and TR

Tahun GDP (M) Kurs GFCF (M) LIR INF TR (M) Trade (M) AK (J) IVA (M)

1986 2,047,293 1,283 525,768 21.49 5.83 14,993 819,473 69 798,545

1987 2,155,799 1,644 554,681 21.67 9.28 18,827 998,819 71 848,963

1988 2,292,815 1,686 618,518 22.1 8.04 21,435 1,083,460 73 907,302

1989 2,501,111 1,770 710,782 21.7 6.42 26,678 1,227,592 75 1,053,730

1990 2,726,250 1,843 825,058 20.83 7.81 37,432 1,441,964 76 1,161,956

1991 2,969,644 1,950 931,494 25.53 9.42 39,098 1,628,540 77 1,277,017

1992 3,184,067 2,030 964,891 24.03 7.53 44,500 1,828,528 79 1,503,687

1993 3,415,042 2,087 1,028,570 20.59 9.69 47,344 1,725,393 82 1,482,120

1994 3,672,538 2,161 1,170,057 17.76 8.52 60,958 1,905,206 84 1,647,643

1995 3,980,898 2,249 1,333,805 18.85 9.43 68,017 2,148,036 88 1,819,329

1996 4,285,149 2,342 1,527,399 19.22 7.97 75,810 2,239,622 90 2,013,806

1997 4,486,546 2,909 1,658,266 21.82 6.23 100,506 2,512,192 90 2,117,949

1998 3,897,609 10,014 1,110,903 32.15 58.39 143,627 3,748,962 92 1,822,466

1999 3,928,444 7,855 908,769 27.66 20.49 179,430 2,472,717 97 1,858,334

2000 4,121,726 8,422 1,060,872 18.46 3.72 99,644 2,944,432 99 1,967,792

2001 4,271,900 10,261 1,129,749 18.55 11.5 190,614 2,981,496 100 2,021,590

2002 4,464,113 9,311 1,182,784 18.95 11.88 215,468 2,637,374 102 2,107,765

2003 4,677,514 8,577 1,189,885 16.94 6.59 249,404 2,507,919 103 2,186,913

2004 4,912,834 8,939 1,364,599 14.12 6.24 283,093 2,935,973 105 2,273,101

2005 5,192,501 9,705 1,513,165 14.05 10.45 312,488 3,322,574 107 2,380,027

2006 5,478,137 9,159 1,552,460 15.98 13.11 343,625 3,103,755 109 2,486,855

2007 5,825,727 9,141 1,697,210 13.86 6.41 374,763 3,194,202 111 2,604,235

2008 6,176,068 9,699 1,898,942 13.6 9.78 658,701 3,616,792 113 2,701,585

2009 6,461,951 10,390 1,961,482 14.5 4.81 619,922 2,940,971 115 2,798,526

2010 6,864,133 9,090 2,127,841 13.25 5.13 723,307 3,205,638 116 2,936,192

2011 7,287,635 8,770 2,316,359 12.4 5.36 873,874 3,656,936 119 3,122,633

2012 7,727,083 9,387 2,527,729 11.8 4.28 980,518 3,831,312 120 3,288,298

2013 8,156,498 10,461 2,654,375 11.66 6.41 1,077,310 3,967,106 122 3,431,081

2014 8,566,271 11,865 2,775,734 12.61 6.39 1,145,283 4,116,716 124 3,577,695

2015 8,976,932 13,389 2,916,602 12.66 6.36 1,164,555 3,764,720 127 3,672,596


Display data in Excel

Open Eviews

Choose Workfile structure type date Frequency Annual Start date 1986 End date 2015

Copy the data (in the red box from B1 ... J ...) in Excel and enter it in Eviews Quick Empty Group (Edit Series)


Unit Root Test Stationarity is one of the important prerequisites in the econometrics model for time series data. Stationary data is data that shows the mean, variance and autovariance (in the lag variation) remain the same at any time the data is formed or used, meaning that with stationary data the time series model can be said to be more stable. If the data used in the model is not stationary, then the data is reconsidered in terms of its validity and stability, because the regression results from non-stationary data will cause spurious regression. Spurious regression is a regression that has a high R2, but there is no meaningful relationship between the two. One of the formal concepts used to determine the stationarity of data is through a unit root test (unit root test). This test is a popular test, developed by David Dickey and Wayne Fuller as the Augmented Dickey-Fuller (ADF) Test. open GDP double klik

View Unit Root Test…

We get the unit root test results for the GDP variable at the level data


From the results of Augmented Dickey-Fuller for the GDP variable on the level data is not stationary because the value of Augmented Dickey-Fuller t-Statistics is still positive, so it is continued with the test on the first difference (first derivative). Graph for non-stationary data can be seen with a click view Graph OK

Obtained graphics as follows:

Stationary Test for first difference data


From the results of Augmented Dickey-Fuller for the GDP variable on the first difference D (GDP) data is stationary because the value of Augmented Dickey-Fuller t-Statistics (-3.104415) is negative and above -3,689194. And the form of a stationary graph as below.

Perform unit root tests for inf, win, exchange rate, GFCF, IVA, TRADE, AK and TR variables

Stationary Non Stationary Non Stationary

Non Stationary Non Stationary Non Stationary

Non Stationary Non Stationary Because all the variables at the data level are not stationary, we do the unit root test for the first data differential.


Stationary Stationary Stationary

Stationary Stationary Stationary

Stationary Stationary All variables are stationary in the first difference data.

Variable

Unit Root Test

Level 1st Difference

ADF Prob Conclusion ADF Prob Conclusion

GDP 2,668 1,0000 Tidak stasione -3,104 0,037 Stasioner

INF -4,484 0,0013 Stasioner -6,447 0,000 Stasioner

LIR -1,587 0,4758 Tidak stasione -5,609 0,000 Stasioner

KURS -0,756 0,8164 Tidak stasione -6,229 0,000 Stasioner

GFCF 0,722 0,9906 Tidak stasione -3,728 0,009 Stasioner

IVA 0,516 0,9844 Tidak stasione -4,545 0,001 Stasioner

TRADE -1,693 0,4240 Tidak stasione -5,999 0,000 Stasioner

AK 2,074 0,998 Tidak stasione -5,225 0,000 Stasioner

TR -0,033 0,948 Tidak stasione -5,2838 0,000 Stasioner


Cointegration Test Two non-stationary time series are co-integrated if they tend to move together all the time. For example, the Fed Funds interest rate and 3-year bond interest rates are non-stationary, while the difference is stationary. In the opaque terminology used in the time series literature, each series is said to be "integrated order 1" or I (1). If two non-stationary series move together through time then we say they are "cointegrated." Economic theory would suggest that they should be bound together through arbitration, but that is not a guarantee, by conducting formal statistical tests. The test procedure is very simple. Regres one variable I (1) on another variable using the least square. Then the residual test (ECT) for non-stationarity uses the Dickey-Fuller test (augmented). If the series is cointegrated, the Dickey-Fuller test statistic will be statistically significant. The null hypothesis is that the residuals are non-stationary. This refusal leads to the conclusion that the residue is stationary and cointegrated series . The steps are as follows:. 1. Do the regression in the long run With the regression equation as follows: GDPt = f(INF, LIR, KURS, AK, GFCF, IVA, TRADE, POP, TR)

GDPt = 0 + 1INFt + 2LIRt + 3KURSt + 5GFCFt + 6IVAt + 7TRADEt + 8AKt + 9TRt Block variables in the order of GDP, INF, LIR, EXCHANGE, GFCF, IVA, TRADE, AK and TR right click Open as Group

The screen will appear as follows:


Click Proc Make Equation…

And the long-term regression equation is as follows: 2. Save Resid

Then from the long-term equation we save the residual in a way click Proc Make residual Series… and save by name ECT


Then obtained ECT

3. Unit Root Test For ECT

Perform unit root tests for ECT and must pass the data level, by clicking view Unit Root Test…


ECT is stationary at the data level

Null Hypothesis: ECT has a unit root

Exogenous: Constant

Lag Length: 1 (Automatic - based on SIC, maxlag=7) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.092864 0.0038

Test critical values: 1% level -3.689194

5% level -2.971853

10% level -2.625121

*MacKinnon (1996) one-sided p-values.

Short-term Regres Then we do a short-term regression (ECM) click Estimate write the ECM equation


So the results of the ECM equation are as follows:

The ECT value is negative and must

be signed


Long-term and Short-term Regression Results

Long-Term Model ECM Model

Variable Coefficient Prob. Variable Coefficient Prob.

INF 4099.527 0.1794 D(INF) 3364.604 0.1641

LIR -6528.040 0.3604 D(LIR) -7690.319 0.2225

KURS 26.18497 0.0711 D(KURS) 20.95405 0.1465

GFCF 0.848454 0.0007 D(GFCF) 0.965011 0.0000

IVA 1.151055 0.0013 D(IVA) 0.813707 0.0038

TRADE -0.200752 0.0029 D(TR) 0.634994 0.0015

TR 0.983500 0.0000 D(TRADE) -0.160247 0.0013

AK 10276.39 0.3073 D(AK) 22510.67 0.1007

ECT(-1) -0.736953 0.0075

C 227912.0 0.7259 C 12201.08 0.7594

R2 0.999306 R2 0,950103

The Classical Assumption Test for the ECM Model The classic assumption test used in linear regression with the Ordinary Least Squared (OLS) approach includes Linearity, Autocorrelation, Heteroscedasticity, Multicollinearity and Normality tests. However, not all classic assumption tests must be performed on every linear regression model using the OLS approach.

1. Linearity tests are hardly carried out on every linear regression model. Because it is assumed that the model is linear. Even if it has to be done solely to see the extent of the linearity.

2. Normality test is basically not a BLUE (Best Linear Unbias Estimator) requirement and some opinions do not require this condition as something that must be fulfilled.

3. Autocorrelation only occurs in time series data. Testing autocorrelation on data that is not time series (cross section or panel) will be useless or meaningless.

4. Multicollinearity needs to be done when linear regression uses more than one independent variable. If only one independent variable, multicollinearity is not possible.

5. Heteroscedasticity usually occurs in cross section data, where panel data is closer to the cross section data characteristics than time series.

Normality test Normality Test is a test conducted with the aim to assess the distribution of data in a group of data or variables, whether the distribution of data is normally distributed or not. Normality Test is useful for determining data that has been collected in normal distribution or taken from a normal population. Open Eviews when we are regressing the ECM model


Choose View Residual Diagnostics Histogram – Normality Test

And the following results are obtained:

See the value of Jarque-Bera and Probability. If Probability> 0.05, Ho is accepted. This means that the data used in analyzing the ECM model has a Normal distribution.


Linearity Test Choose View Stability Diagnostics Ramsey RESET Test…

Click OK

See F-Statistics, if the probability is above 0.05, it means that the model used has fulfilled the Linearity assumption.

Autocorrelation Test Autocorrelation is the correlation between members of a series of observations sorted by time (time series). Autocoreation causes the residual variance to be obtained lower than it should be, resulting in R2 being higher than it should be. Besides testing hypotheses using t-statistics and F-statistics will be misleading. Choose View Residual Diagnostics Serial Correlation LM Test…


Obs * R-squared value less than 0.05 means that the model contains autocorrelation. Or An F-statistic value above 0.05 means that the model does not contain this autocorrelation used. (Because the results of Obs * R-squared are not consistent with F-statistics, we choose those who accept Ho ie the model does not contain Autocorrelation).

Heteroscedasticity Test One of the assumptions that must be met for the estimated parameters in the regression model to be BLUE is that var (ui) must be equal to σ2 (constant), or in other words, all residuals or errors have the same variant. Such conditions are called homoscedastic. Meanwhile, if the variant is not constant or changing is called heteroscedastic. Formal tests for this problem include the Breusch-Pagan-Godfrey Test, the Harvey Test, the Glejsyer Test, the ARCH Test and the Custom Test Wizard. This test can be done directly with the EViews program. Choose View Residual Diagnostics Heteroscedasticity Test…


For hetero schedasticity test, you can choose several methods such as Breusch-Pagan-Godfrey, Harvey, Glejsyer, ARCH and Custom Test Wizard

Choose Breusch-Pagan-Godfrey


Obs * R-squared value 0.7627 More than 0.05 means that the model does not contain heteroscedasticity. Or Prob F-statistic value 0.839 above 0.05 means that the model does not contain heteroscedasticity Homoscedasticity (Obs * R-squared results are consistent with F-statistics. All results receive Ho, ie the model does not contain heteroscedasticity)

Multicollinearity Test Multicollinearity is the correlation between independent variables with other independent variables. Consequently, even though the estimation results are still BLUE (Best Linear Unlimited Estimator), multicollinearity can cause a larger standard error, the coefficient of determination (R2) remains high and the F-stat test is significant even though there are many insignificant variables. In other words, it can be said that an equation model states that there is a multicollinear disturbance if R2 is high but there is little or no significant independent variable on t-statistic testing. By using the Correlation Matrix open Eview Block variables according to sequence Right-click Copy


open Quick Group Statistics Correlation

Click OK

The model avoids Multicollinearity if the Correlation value is less than 0.8 Or use VIF and TOL The multicollinearity test is used to assess whether there is a correlation or intercorrelation between independent variables in the regression model or is also commonly used to determine whether or not there


is a deviation from the classic assumption of multicollinearity, namely the existence of a linear relationship between the independent variables in the regression model. In testing the presence or absence of multicollinearity symptoms is done by looking at the value of VIF (Variance Inflation Factor) and Tolerance. Hypothesis: H0: There is a multicollinearity problem H1: There is no multicollinearity problem Probability <10, H0 rejected, H1 accepted Probability> 10, H1 rejected, H0 accepted Open Coefficient Diagnostics Variance Inflation Factors

And the results of the Multicollinearity test with VIF are as follows:


All Centered VIF values are less than 10, so it can be concluded that the ECM model does not contain Multicollinearity. Interpretation of Regression Results The ECM model that we produce has fulfilled all the criteria, so that it can be followed by an analysis of the results of the short-term and long-term regressions.

Long-Term Model ECM Model

Variable Coefficient Prob. Variable Coefficient Prob.

INF 4099.527 0.1794 D(INF) 3364.604 0.1641

LIR -6528.040 0.3604 D(LIR) -7690.319 0.2225

KURS 26.18497 0.0711 D(KURS) 20.95405 0.1465

GFCF 0.848454 0.0007 D(GFCF) 0.965011 0.0000

IVA 1.151055 0.0013 D(IVA) 0.813707 0.0038

TRADE -0.200752 0.0029 D(TR) 0.634994 0.0015

TR 0.983500 0.0000 D(TRADE) -0.160247 0.0013

AK 10276.39 0.3073 D(AK) 22510.67 0.1007

ECT(-1) -0.736953 0.0075

C 227912.0 0.7259 C 12201.08 0.7594

R2 0.999306 R2 0,950103

Fh 3.777,227 Fh 40,198

DW 1,633 DW 1,40617

Note: GDP is dependent variable In the long run, those affecting GDP are Exchange Rate, GFCF, Trade, and TR. Whereas in the short term that affect GDP growth are changes in the GFCF, changes in IVA, changes in TR and changes in trade. Trade (trade) in the short term and long term affects negatively, meaning that the higher the value of Indonesia's trade transactions with other countries will reduce GDP. If the TRADE value increases by 1 billion Rupiah, it will decrease GDP by (1 billion times -0.160247) 160 million rupiahs, while in the long term it will decrease by (1 billion times -0.200752) 200 million rupiahs. In the long run, the influence of TRADE must be considered by the government. ECT value According to Widarjono (2007) the coefficient of correction for an ECT imbalance is called a disequilibrium error. Therefore, if ECT is equal to zero, of course Y and X are in equilibrium. The results of these values explain how quickly the time needed to get the balance value. In principle, the error correction model has a long-term fixed balance between economic variables. If in the short term there is an imbalance in one period, then the error correction model will correct it in the next period (Engle and Granger, 1987). To state whether the ECM model used is valid or not, then the Resid coefficient (-1) or ECT must be significant. If this coefficient is not significant, then the model is not suitable and further model specification changes need to be made.


Based on the Table, it is known that the Error Correction Term (ECT) coefficient in the model is significant = 0.0075 <0.05, which indicates that the Error Correction Model (ECM) used is valid. While the equilibrium value of -0.736953 can be interpreted that the process of adjusting to the imbalance of GDP changes (Economic Growth) for the period of 1986-2015 is relatively slow. ECT value of -0.736953 means that if there is a past imbalance of 100%, then changes in GDP (Economic Growth) will adjust to a decrease of 73.69%. Thus it can be interpreted that Economic Growth takes 7-8 years to achieve a full balance (100%) of changes in GDP (Economic Growth). Determination Coefficient Test (R2) Test the coefficient of determination to see how much influence the changes in the independent variables (independent variables) used in the model are able to explain their influence on the dependent variable (dependent variable). This test looks at the coefficient of determination (R2) obtained from the estimated equation.


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ERROR CORRECTION MODEL - WordPress.com€¦ · ERROR CORRECTION MODEL Yule (1936) and Granger and Newbold (1974) were the first to draw attention to the problem of false correlations